ArticleChemicke zvesti2017
SCRAMBLE'N'GAMBLE: a tool for fast and facile generation of random data for statistical evaluation of QSAR models.
Article in Chemicke zvesti, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Understanding the Enzyme (Journal of chemical information and modeling · 2024Article
- Protein characteristics substantially influence the propensity of activity cliffs among kinase inhibitors.Scientific reports · 2024Article
- Viral Immunogenicity Prediction by Machine Learning Methods.International journal of molecular sciences · 2024Article
- Combining machine learning and structure-based approaches to develop oncogene PIM kinase inhibitors.Frontiers in chemistry · 2023Article
- Cell Morphological Profiling Enables High-Throughput Screening for PROteolysis TArgeting Chimera (PROTAC) Phenotypic Signature.ACS chemical biology · 2022Article
- Molecular insights on ABL kinase activation using tree-based machine learning models and molecular docking.Molecular diversity · 2021Article
- Development of Machine Learning Models for Accurately Predicting and Ranking the Activity of Lead Molecules to Inhibit PRC2 Dependent Cancer.Pharmaceuticals (Basel, Switzerland) · 2021Article
- Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure.Biology direct · 2021Article
- Docking-generated multiple ligand poses for bootstrapping bioactivity classifying Machine Learning: Repurposing covalent inhibitors for COVID-19-related TMPRSS2 as case study.Computational and structural biotechnology journal · 2021Article
- Molecular Topology QSAR Strategy for Crop Protection: New Natural Fungicides with Chitin Inhibitory Activity.ACS omega · 2020Article
- Overcoming randomness does not rule out the importance of inherent randomness for functionality.Journal of biosciences · 2019Review
Corrections and comments
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Authors and funding
2 authors.
Funding
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Abstract
A common practice in modern QSAR modelling is to derive models by variable selection methods working on large descriptor pools. As pointed out previously, this is intrinsically burdened with the risk of finding random correlations. Therefore it is desirable to perform tests showing the performance of models built on random data. In this contribution, we introduce a simple and freely available software tool SCRAMBLE'N'GAMBLE that is aimed at facilitating data preparation for
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